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| """
功能损伤检测器
检测驾驶员的驾驶能力损伤，而非醉酒状态
"""
import numpy as np
class FunctionalImpairmentDetector:
"""
功能损伤检测器
通过行为指标判断驾驶能力损伤
"""
def __init__(self):
self.indicators = {
'eye_movement': {
'weight': 0.3,
'metrics': ['saccade_velocity', 'fixation_stability', 'pupil_response']
},
'steering_behavior': {
'weight': 0.3,
'metrics': ['correction_frequency', 'jerk', 'micro_corrections']
},
'lane_keeping': {
'weight': 0.2,
'metrics': ['deviation_std', 'correction_latency']
},
'response_time': {
'weight': 0.2,
'metrics': ['brake_reaction', 'steering_reaction']
}
}
self.impairment_threshold = 0.7
def detect(
self,
eye_data: dict,
steering_data: dict,
lane_data: dict,
response_data: dict
) -> dict:
"""
检测功能损伤
Args:
eye_data: 眼动数据
steering_data: 转向数据
lane_data: 车道保持数据
response_data: 响应时间数据
Returns:
{
'is_impaired': bool,
'impairment_score': float,
'cause_hints': list,
'recommendation': str
}
"""
eye_score = self._evaluate_eye_movement(eye_data)
steering_score = self._evaluate_steering(steering_data)
lane_score = self._evaluate_lane_keeping(lane_data)
response_score = self._evaluate_response(response_data)
total_score = (
eye_score * self.indicators['eye_movement']['weight'] +
steering_score * self.indicators['steering_behavior']['weight'] +
lane_score * self.indicators['lane_keeping']['weight'] +
response_score * self.indicators['response_time']['weight']
)
is_impaired = total_score < self.impairment_threshold
cause_hints = self._infer_cause(eye_score, steering_score, response_score)
if is_impaired:
if total_score < 0.3:
recommendation = '严重损伤，建议停车休息'
elif total_score < 0.5:
recommendation = '明显损伤，需要警告'
else:
recommendation = '轻度损伤，持续监控'
else:
recommendation = '驾驶能力正常'
return {
'is_impaired': is_impaired,
'impairment_score': 1 - total_score,
'cause_hints': cause_hints,
'recommendation': recommendation,
'breakdown': {
'eye_movement': eye_score,
'steering': steering_score,
'lane_keeping': lane_score,
'response': response_score
}
}
def _evaluate_eye_movement(self, data: dict) -> float:
"""评估眼动指标"""
score = 1.0
saccade_velocity = data.get('saccade_velocity', 100)
if saccade_velocity < 50:
score -= 0.3
elif saccade_velocity > 200:
score -= 0.2
fixation_stability = data.get('fixation_stability', 1.0)
score *= fixation_stability
pupil_response = data.get('pupil_response', 1.0)
if pupil_response < 0.3:
score -= 0.3
return max(0, score)
def _evaluate_steering(self, data: dict) -> float:
"""评估转向指标"""
score = 1.0
micro_corrections = data.get('micro_corrections', 10)
if micro_corrections > 30:
score -= 0.3
elif micro_corrections < 5:
score -= 0.4
jerk = data.get('jerk', 0.1)
if jerk > 0.5:
score -= 0.3
return max(0, score)
def _evaluate_lane_keeping(self, data: dict) -> float:
"""评估车道保持"""
deviation_std = data.get('deviation_std', 0.1)
score = max(0, 1 - deviation_std * 5)
return score
def _evaluate_response(self, data: dict) -> float:
"""评估响应时间"""
score = 1.0
brake_reaction = data.get('brake_reaction', 0.5)
if brake_reaction > 1.5:
score -= 0.4
elif brake_reaction > 1.0:
score -= 0.2
return max(0, score)
def _infer_cause(
self,
eye_score: float,
steering_score: float,
response_score: float
) -> list:
"""推断可能原因"""
causes = []
if eye_score < 0.5:
causes.append('可能：疲劳 / 药物 / 酒精')
if steering_score < 0.5 and response_score < 0.5:
causes.append('可能：疲劳 / 睡眠不足')
if steering_score < 0.5 and response_score > 0.7:
causes.append('可能：酒精 / 药物')
return causes
|